(2024) 22:318
Øien et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03523-8

BMC Medicine

RESEARCH ARTICLE

Open Access

All‑cause mortality and hospital admissions
for nursing home residents during the COVID‑19
pandemic: a Norwegian register‑based cohort
study
Henning Øien1,3*, Jonas Gjesvik2, Katrine Damgaard Skyrud1, Torill Alise Rotevatn1 and Mari Grøsland1

Abstract
Background This paper investigates the consequences of the COVID-19 pandemic on mortality and hospitalization among nursing home residents in Norway. While existing evidence shows that nursing home residents wereoverrepresented among COVID-19-related deaths, suggesting inadequate protection measures, this study argues
that the observed overrepresentation in mortality and hospitalization may partly stem from the inherent frailty of thisdemographic. Using nationwide administrative data, we assessed excess deaths and hospitalization by comparing
pandemic-era rates to those of a pre-pandemic cohort.
Methods We compared mortality and hospitalization rates between a pandemic cohort of nursing home residents
as of September 2019 (N = 30,052), and a pre-pandemic cohort as of September 2017 (N = 30,429). Both cohorts were
followed monthly for two years, beginning in September 2019 and 2017, respectively. This analysis was conductedat the national level and separately for nursing home residents in areas with low, medium, and high SARS-CoV-2 community transmission. Event studies and difference-in-difference models allowed us to separate the impact of the pandemic on mortality and hospitalization from secular and seasonal changes.
Results The pandemic cohort experienced a non-significant 0.07 percentage points (95% confidence interval(CI): − 0.081 to 0.221) increase in all-cause mortality during the 18 months following pandemic onset, comparedto the pre-pandemic cohort. Moreover, our findings indicate a substantial reduction in hospitalizations of 0.27 percentage points (95% CI: − 0.464 to − 0.135) and a non-significant decrease of 0.80 percentage points (95% CI: − 2.529
to 0.929) in the proportion of nursing home residents hospitalized before death. The effect on mortality remained
consistent across regions with both high and low levels of SARS-CoV-2 community transmission.
Conclusions Our findings indicate no clear evidence of excess all-cause mortality in Norway during the pandemic,neither nationally nor in areas with high infection rates. This suggests that early implementation of nationwide
and nursing home-specific infection control measures during the pandemic effectively protected nursing homeresidents. Furthermore, our results revealed a decrease in hospitalizations, both overall and prior to death, suggestingthat nursing homes adhered to national guidelines promoting on-site treatment for residents.
Keywords Nursing home residents, Long-term care, COVID-19, Pandemic, Mortality, Hospital admissions*Correspondence:
Henning Øienhenning.oien@fhi.no
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Øien et al. BMC Medicine(2024) 22:318

Background
In March 2020, governments worldwide implemented
stringent social distancing measures and stay-at-homeorders in response to the spread of the SARS-CoV-2
virus that caused the COVID-19 pandemic. Nursing
homes, housing frail older people, were no exception tothese measures, aligning with recommendations fromthe World Health Organization [1]. Despite these efforts,
the pandemic response in American and European nursing homes was criticized because nursing home residents
were highly overrepresented among those who diedof COVID-19 [2–4]. During the initial year of the pandemic, nursing home residents, constituting a small fraction of the population (less than 0.5% in the United States(US) and 1% in Europe), accounted for a disproportionately high share of COVID-19 deaths, reaching up to
25% in the US [2], and 31 to 80% of COVID-19 deaths inEurope [3].
Although there is clear evidence of structural problems
in the pandemic response in US and European nursing homes [2, 4–7], it is expected that without extreme
mitigation measures, elderly and frail nursing home residents would be disproportionately affected by respiratory diseases, including COVID-19. Frail older people are
typically multimorbid, have weakened immune systems,
and frequently exhibit atypical disease symptoms [8].
This makes it harder to detect COVID-19 among nursing home residents and to prevent infection entry andspread in nursing homes without imposing strict mitigation measures [9]. These factors plausibly contributed
to the overrepresentation of nursing home residents
among COVID-19 fatalities in countries with relatively
high rates of community transmission (such as Sweden,
the US, and the United Kingdom (UK) [10–13]) as wellas in countries with relatively low rates (such as Canada,
Denmark, and Norway [14, 15]). Since deaths and hospitalizations are common among nursing home residents,a better way to assess the impact of the COVID-19 pandemic on the nursing home population is to compare therates of all-cause mortality and hospitalization of nursing home residents during the pandemic to residents in a
pre-pandemic period [16].
There is some evidence of excess all-cause mortality and hospitalizations among nursing home residents.
Studies in Sweden, Italy, England, Canada, and the US
have reported higher all-cause mortality rates in longterm care facilities during the pandemic [10–12, 14, 17].
Additionally, evidence indicates a decrease in hospitalization rates among residents in skilled nursing facilities in
the US during the pandemic [11, 18].
Learning how COVID-19 has affected nursing homeresidents in countries with varying transmission rates
and mitigation strategies is important. This can help

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policymakers and public health authorities create targeted interventions to safeguard residents and preventthe spread of disease in future pandemics. This study
aimed to contribute to our understanding of the impactof the COVID-19 pandemic on nursing home residents
by comparing mortality and hospitalization rates in preand post-pandemic cohorts in Norway. To investigate theconsequences of the pandemic, administrative data for
all Norwegian nursing home residents as of September
2019 were analyzed, tracking their mortality rates andhealthcare utilization from September 2019 to August2021. Using difference-in-differences and event study
models, these outcomes were compared to a pre-pandemic cohort of nursing home residents in September
2017. We further analyzed cause-specific hospitalizations to identify which conditions experienced shifts inadmission rates during the pandemic. Given the significant variation in SARS-CoV-2 infection rates across different regions in Norway, we categorized nursing homeresidents based on the community transmission rates
in their respective regions—low, medium, and high. We
then examined excess mortality and hospitalization rates
within each category to assess the impact of transmission
intensity on these outcomes.

Methods
Institutional setting

In Norway, universal health coverage ensures that all
residents have access to highly subsidized specialized
and primary healthcare services. The 356 municipalities
are responsible for primary care, nursing home care, andother long-term care services. Nursing homes are staffedwith physicians, nurses, and other healthcare workers
and offer 24-h health and care services for those who areunable to live independently [19, 20]. Over time, nursing
home care has almost exclusively become a service forolder people with severe physical or cognitive impairment. This is driven by the principle that people withfunctional disabilities should be given services at the“lowest level of efficient care” to be able to live at home aslong as possible (20, 21).
COVID‑19 mitigation measures

During the pandemic, comprehensive national lockdowns were instituted for the entire population, including stay-at-home orders, limitations on visitor numbers,
and social distancing restrictions, alongside rigorous testing, quarantine, and isolation procedures [21]. Throughout our pandemic study period, the strictest measures
were implemented at the onset of the pandemic and during the winter and spring of 2020/2021. In contrast, thesummer months of both 2020 and 2021 were characterized by a gradual reopening of society and a relaxationØien et al. BMC Medicine(2024) 22:318

of measures. Community mitigation measures werealso periodically adjusted according to local infection
rates, where high infection rates led to stricter measures[21]. In hospitals, guidelines mandated a prioritizationof acute healthcare services [22], resulting in intermittent declines in elective admissions [23]. In instanceswhere demand for intensive care exceeded the available
capacity, guidelines prioritized patients with the highest
expected benefit from treatment [24].
The municipalities were responsible for controllingthe virus within their communities and nursing homes.
To ensure a consistent response nationwide, the central
government also issued nursing home-specific guidelines. The guidelines recommended that nursing homes
should introduce measures to limit visitor access, implement enhanced cleaning and infection control protocols,and restrict hospital admissions for their residents. These
steps were taken to increase hospital capacity and minimize the risk of in-hospital SARS-CoV-2 transmission[25, 26]. Screening measures for both staff and residents
were also implemented. However, in the initial months ofthe pandemic, a shortage of SARS-CoV-2 tests resulted
in a focus on testing symptomatic cases [27]. This limitation may have contributed to insufficient identification of asymptomatic individuals, potentially leading tothe occurrence of clusters of infection in nursing homes
at the onset of the pandemic. From May 2020, there wastherefore a concerted effort to prioritize and enhance theuse of SARS-CoV-2 tests in nursing homes, enabling amore comprehensive testing approach that also included
asymptomatic cases [27].
Due to increased risk of severe disease and close interaction with health personnel, nursing home residents
were given high priority when Norway received its initial doses of the vaccine in December 2020 [28]. By
21 February 2021, 91% of nursing home residents hadreceived their first dose, and 82% had received two doses[29]. While most of the nursing home-specific measures remained in effect throughout our pandemic study
period, there were some relaxations in February 2021
when most of the nursing home population was vaccinated [21]. A review of experiences at Norwegian nursing
homes during the pandemic uncovered that most facilities successfully prevented initial entry of the virus, butthat containing further transmission proved challenging once the virus was present within the facility. As aresult, the majority of infections and COVID-19 fatalities
among nursing home residents were concentrated withina select few nursing homes [30].
Data

To investigate mortality and hospitalization rates among
nursing home residents during the pandemic, we used

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nationwide individual-level registry data from the Norwegian Emergency Preparedness Register, Beredt C19[31], originating from the following registers: the Norwegian Population Register (demographic characteristics,
including date of birth and gender); Individual-based
Statistics for Nursing and Care Services, the NorwegianPatient Registry and the Norwegian Surveillance System for Communicable Diseases. The data sources werelinked using a deidentified version of the personal identification number received upon birth or immigration to
Norway.
Study sample

Our pandemic cohort included all individuals who werepermanent residents of nursing homes as of September
1st, 2019, totaling 30,052 people. They were fully exposedto the pandemic from its onset in March 2020. For comparison, we examined a pre-pandemic cohort consistingof 30,429 permanent nursing home residents on September 1st, 2017. Individuals in both cohorts were observed
for up to 24 months or until death, from September 2017
to August 2019, and September 2019 to August 2021,
respectively. This allowed us to analyze outcome trends
6 months before and 18 months after the pandemic onset.
The pandemic might influence the composition of nursing home residents itself. For instance, heightened healthconcerns, changes in care availability and visitation
restrictions might influence the entry of residents into
nursing homes during the pandemic. In theory, it may
also influence discharges from nursing homes, however,since we only considered long-term nursing home residents who required 24/7 supervision this is less likely. To
avoid such compositional effects, we maintained a fixedcohort design by ensuring that the composition of nursing home residents in the analysis remained unchanged
from their initial inclusion in September 2019 for the
pandemic cohort, and September 2017 for the pre-pandemic cohort. No new admissions were accounted for,and no discharges were permitted, except in the event ofdeath, thereby preserving the consistency of our sample
for the duration of the 24-month follow-up period.
Outcomes

Our primary outcomes of interest were all-cause mortality, all-cause inpatient hospitalization, and all-cause
hospitalization before death. Inpatient hospitalizations
were identified as all-cause overnight hospitalizations
and included both acute and elective hospitalizations.
Additionally, we studied the five most common causespecific conditions for (inpatient) hospitalizations fornursing home residents (sickness in the circulatory system, respiratory system, digestive system, genitourinary system, injury, and other external causes) using theØien et al. BMC Medicine(2024) 22:318

International Classification of Diseases, version 10 (ICD10). We collapsed hospital admissions with less than
two days between admission and readmission into a single hospitalization stay. In the analysis of cause-specifichospitalizations, we applied the main diagnosis of thefirst admission, as this was most likely the diagnosis thatcaused the nursing home to transfer the patient to thehospital.
We defined three indicators for our sample of nursing home residents: the Mortality Indicator is “1” in theresident’s month of death and “0” otherwise; the Hospitalization Indicator is “1” for any month with one or more
overnight hospital stays, and “0” in other months; and the
Hospitalization Prior to Death Indicator, which is “1” if a
hospitalization occurred either in the month leading up
to or during the month of death, and “0” otherwise.
COVID‑19 pandemic

Our intervention of interest was the COVID-19 pandemic. We treated the first lockdown in March 2020 as
the start of the pandemic. To separate the effect of thepandemic from cyclical trends, we compared the change
in mortality or hospitalization of nursing home residents
in the pandemic cohort to outcome changes in the prepandemic cohort over the same period two years prior.
To do this, we compared outcomes between the pandemic and pre-pandemic cohorts across a centered duration variable. The duration variable was centered in thelast month before the pandemic started, February 2020,
for the pandemic cohort, and February 2018, for the prepandemic cohort.
SARS-CoV-2 infection rates varied significantly in different regions in Norway [32]. The spread was higher indensely populated areas. We utilized this variation to
explore differences in mortality and hospitalization rates
between pandemic and pre-pandemic cohorts withinareas with high and low community transmission rates.
The measure of SARS-CoV-2 community transmission
was defined as the total number of notified infections
in each municipality in 2020 and 2021, divided by the
total number of inhabitants in the same municipality.
We linked each nursing home resident to the community
transmission rates in the municipality where the nursing
home was located. We then classified nursing home residents into low, medium, and high community transmission rates: those in the lowest 25% were placed in the lowtransmission group, those in the highest 25% in the hightransmission group, and the rest in the medium group.
Incidence rates ranged from 0 to 3.48 per 100 inhabitants
in low, 3.49 to 8.74 in medium, and 8.74 to 14.3 in hightransmission areas. See Additional file 1: Supplementary
Fig. 1 for a map of these groups across Norway.

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Statistical analysis

We used the following event-study approach to examine
the impact of the pandemic on nursing home residents’mortality and hospitalization:

yicm = αc + γm +18k=−5,� =−2

πk Dck + εicm,(1)where yicm represents an indicator for either mortality or hospitalization of individual i in cohort c in relative month m. The term αc accounts for cohort-specificeffects, controlling for unobservable effects that are constant to cohorts over time, and γm is a relative monthspecific effect that controls for seasonal effects that arecommon to both cohorts. Dck are relative month indicators equal to 1 for the pandemic cohort when month mis month k (k ∈ [1,18]) of the pandemic or when there
are k months (k ∈ [−5,0]) before the pandemic arrives.
The error term, εicm, is clustered at the individual levelto account for within-individual correlation over time.
The relative time index k is set to 0 for February 2020 for
the pandemic cohort and February 2018 for the pre-pandemic cohort.
By excluding k =  − 2, we designate this month (December 2019 for the pandemic cohort and December 2017 forthe pre-pandemic cohort) as the reference month.1 Consequently, the relative time indicators’ (πk ) measure thechange in outcomes from the reference month (k =  − 2)to the specific months (k ranging from − 5 to − 3 and − 1
to 18) in the pandemic cohort relative to the change inoutcomes for the pre-pandemic cohort over the corresponding months two years prior.2
These average comparisons would represent the pandemic’s impact if the change in outcomes for the prepandemic cohort represents how the outcomes would
have changed for the pandemic cohort if the pandemic
had not happened. Essentially, the changes in the prepandemic cohort outcomes are considered to reflectchanges that would have happened anyway, without
the pandemic. The assumption that the trends for bothcohorts would have been equal without the pandemic, is1

We need a reference month to compare changes in outcomes between the
pandemic and pre-pandemic cohort. The reason we chose k =  − 2 rather
than the more common k =  − 1 is because the period immediately preceding the pandemic is more likely to be influenced by anticipation and early
effects of the incoming pandemic.

2
For example, the relative time coefficient for k = 1 measures the differencein outcomes of interest for the pandemic cohort in March 2020 comparedto the baseline month (December 2019 (k = -2)) minus the change in outcomes for the pre-pandemic cohort during the same period two years prior(March 2018 (k = 1) compared to December 2017 (k =  − 2)). This coefficienthelps isolate the impact of the pandemic by accounting for changes thatwould have occurred regardless of the pandemic.

Øien et al. BMC Medicine(2024) 22:318

known as the “parallel trends assumption,” which is theidentifying assumption in event studies, as discussed in
Cunningham’s book in chapter 9.4 [33].
To assess the parallel trend assumption, we tested
whether there were significant differences in the outcomes in the pre-pandemic period (September 2019 toFebruary 2020; k =  − 5, − 4, − 3…,0) for the pandemic
cohort compared to the same seasonal period two years
prior for the pre-pandemic cohort (September 2017 to
February 2018; k =  − 5, − 4, − 3…,0).
Difference‑in‑differences

Monthly mortality and hospitalization rates are likely
to be influenced by short-term factors that could causesignificant variation from one month to the next, whichchallenge the separation of any pandemic effects fromnatural fluctuations in monthly mortality. Therefore, we
also estimate difference-in-differences (DD) models aswell as a model where we grouped the months during the
pandemic into seasons. In the former model, we compare
the average pre-to-post pandemic percentage change forthe pandemic cohort to the same period change for the
pre-pandemic cohort two years prior.3 While in the lattermodel, we look at changes from the pre-pandemic period
to individual seasons during the pandemic.4 The identifying assumption in the DD models is the same parallel
trends assumption as in the event study, which posits thatin the absence of the pandemic, the pandemic cohort andthe pre-pandemic cohort would have followed the same
trend over time in our outcome variables [33].
The event study and DD models capture the dynamic
effects over time, allowing for an examination of mortality trends before and during the pandemic. In thesemodels, there is a possibility of selective survival effects,
where periods of increased mortality might lead to subsequent periods with relatively healthier population andconsequently lower mortality. To systematically assess
the net impact of such fluctuations on overall mortality,
we utilized Kaplan–Meier Survival curves [34]. These
curves will compare the survival probabilities of the pandemic and pre-pandemic cohorts over time. The comparison is statistically analyzed using the log-rank test to3

This is calculated as follows: ((monthly percentage outcome averaged
over the pandemic period for the pandemic cohort) − (monthly percentage outcome averaged over the pre-pandemic period for the pandemic
cohort) − ((monthly percentage outcome averaged over the post-period forthe pre-pandemic cohort) − (monthly percentage outcome averaged overthe pre-period period for the pre-pandemic cohort)).

4

The model includes separate estimates for each season from March 2020
to February 2022 (March–May (spring); June–August (summer); September–November (fall); and December–February (winter)) and compared it to
the 6 months before the pandemic. The estimating equation for this model
is equal to Eq. (1), but where the relative time indicators are replaced by theseason indicators.

Page 5 of 14

determine the significance of any differences observed
between the two cohorts’ survival rates.
Additionally, we investigated whether COVID-19 mortality and hospitalization rates for nursing home residents differed according to different levels of community
transmissions. More specifically, we compared outcomesfor nursing home residents in municipalities with low,
middle, and high community transmissions in the pandemic cohort, with outcomes for nursing home residents
in the same municipalities in the pre-pandemic cohort.
All regression estimates were presented for each monthor season as a change in percentage points.
Statistical analyses were conducted using R version
4.2, and the modeling and clustering of standard deviations were performed using the “fixest” package with thevcov option [35], while the Kaplan–Meier survival curve[34] was calculated using the function ggsurvplot in the“survminer” package in R. The study adheres to the mainprinciples of the STROBE guidelines for cohort studies[36].

Results
Descriptive statistics

In our study, we used the pre-pandemic cohort of nursing
home residents as a baseline comparison group for those
exposed to the pandemic. The fundamental assumptionof our model is that, in the absence of the pandemic,
health outcomes for the pandemic cohort would evolvein parallel to the pre-pandemic cohort. To assess thevalidity of this assumption, we compared both cohorts
in terms of key demographic characteristics and levelof community transmission before the pandemic. Our
analysis revealed notable similarities between the groups:
the average age was 85 years in both cohorts, and womenconstituted 70% of each cohort, as detailed in Table 1.
The average health outcomes of our cohorts in themonths before the pandemic (September 2017–February
2018 for the pre-pandemic cohort and September 2019–
February 2020 for the pandemic cohort) were also similar(Table 2). The pre-pandemic and pandemic cohorts werethus similar in both demographics and health status, and
it is therefore reasonable to expect that these cohorts
would show similar trends in mortality and hospitalizations had the pandemic not occurred.
The table shows the distribution of age, gender, and the
SARS-CoV-2 community transmission level of the prepandemic and the pandemic cohort. The level of community transmission was calculated as the total number of
SARS-CoV-2 infections in each municipality in 2020 and
2021, divided by the total number of inhabitants. Nursing
home residents were divided into three groups, based on
the community transmission in their municipality.

Øien et al. BMC Medicine(2024) 22:318

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Table 1 Descriptive statistics on group characteristics for prepandemic and pandemic nursing home residents
Pre-pandemic cohort

Pandemic cohort

Sample characteristics
Residents, N

30,42930,052Age, mean (SD)84.6 (8.9)

84.6 (8.8)

Gender, N (%)
Females

21,372 (70.2)

20,970 (69.8)

Males9057 (29.8)

9082 (30.2)

Level of community transmission, N (%)
Low7782 (25.6)

7409 (24.7)

Medium

15,059 (49.5)

15,225 (50.7)

High7588 (24.9)

7418 (24.7)

Crude health outcomes for nursing home residents
over time

In Fig. 1, we plotted the crude trends in mortality, hospitalization, and hospitalization before death in the prepandemic and pandemic cohorts. In the left panel, weobserve that the trend in mortality for the pandemic
cohort (solid line) from September 2019 (relative month(k) =  − 5) to February 2020 (k = 0) was similar to the outcome trend for the pre-pandemic cohort from September
2017 (k =  − 5) to February 2018 (k = 0) (dashed line).
After the pandemic arrived, the mortality trend in
the pandemic cohort continued to follow the trend of
the pre-pandemic cohort. In total, the monthly mortality rate for nursing home residents in the pandemic
cohort slightly decreased from 3.49% in the pre-treatment period to 3.32% in the post-treatment period, corresponding to a total decrease of 0.17 percentage points(Table 2). For the pre-pandemic cohort, the mortality
rate decreased from 3.53 to 3.29%, which corresponds toa 0.24 percentage points decrease. Hence, the difference
in mortality from the pre- and post-treatment period
between the pre-pandemic and the pandemic cohort was
0.07 percentage points (see the note in Table 2 for theformula behind the simple DD estimate) (Table 2). This
increase was not statistically significant.
In contrast, there was a noticeable decrease of 0.27
percentage points in hospitalizations following the pandemic’s onset (Fig. 1, Table 2). This trend implies that
nursing homes adhered to revised guidelines designed
to minimize hospital admissions among their residents

Table 2 Difference-in-differences of monthly mortality and healthcare utilization from before to after the onset of the pandemic
Pre-pandemic cohort

Pandemic cohort

Sep 2017–Feb
2018

Mar 2018–Aug
2019

Sep 2019–Feb
2020

Mar 2020–Aug
2021

Simple DD
estimatea

All-cause mortality 3.53

3.29

3.49

3.32

0.07

All-cause hospitali‑ 3.34
zations

2.68

3.22

2.29

− 0.27***

Health outcome,monthly %ICD-10 codes:

Cause-specific hospitalizations (ICD-10 chapter)
Sickness in the cir- 0.42
culatory system

0.34

0.38

0.27

− 0.03I

Sicknessin the respiratory
system

0.64

0.50

0.55

0.26

− 0.15***J

Sicknessin the digestivesystem

0.30

0.24

0.29

0.21

− 0.02K

Sicknessin the genitourinary
system

0.32

0.29

0.34

0.26

− 0.06**N

Injury, poisoning,and certain otherconsequencesof external causes

0.66

0.53

0.67

0.58

0.04

S

Hospitalization
prior to death

20.2

17.3

18.4

14.7

− 0.80

Person-months:

167,557

336,310

165,159

331,555a

The estimate equals the difference between percentage change in monthly health outcome from before to during the pandemic for the pandemic cohort, and thecorresponding percentage change in the pre-pandemic cohort, calculated as follows: ((monthly percentage outcome averaged over the pandemic period for the
pandemic cohort) − (monthly percentage outcome averaged over the pre-pandemic period for the pandemic cohort) − ((monthly percentage outcome averaged overthe post-period for the pre-pandemic cohort) − (monthly percentage outcome averaged over the pre-period period for the pre-pandemic cohort)). Standard errors(SEs) are clustered on individuals. Stars are used to denote the conventional levels of statistical significance, where * corresponds to a p-value of 0.1 or less, ** to a
p-value of 0.05 or less, and *** to a p-value of 0.01 or lessØien et al. BMC Medicine(2024) 22:318

Page 7 of 14

Mortality

Hospitalization

Hospitalization before death

5.0

30

4

Percent (%)

4.5

4.0

20
3

3.5

3.0

10

2

2.5
−5

0

5

10

15

−5

0

5

10

15

−5

0

5

10

15

Relative month
Pandemic Cohort

Pre−Pandemic Cohort

Fig. 1 Crude monthly percent (%) of mortality and healthcare utilization among nursing home residents. Monthly percent (%) of nursing homeresidents who died, were hospitalized, and were hospitalized before death in the pandemic cohort (solid lines) and the pre-pandemic cohort(dashed lines). The x-axis refers to the relative month (k). The pre-pandemic cohort was measured 24 months earlier. The dotted vertical line (k = 0)refers to February 2018 for the pre-pandemic cohort, and February 2020, which is the month before the onset of the pandemic, for the pandemic
cohort(see the “Background” section for further details on theguidelines). Furthermore, it is plausible that the lockdown mitigated the transmission of infectious diseases,
thereby diminishing the demand for hospital-basedcare for individuals in nursing homes. Supporting evidence for this interpretation is presented in Table 2 andAdditional file 1: Supplementary Fig. 2, which showsa consistent decline in hospitalization rates for respiratory diseases throughout the study period. There
was also a statistically significant reduction in hospitalizations due to sickness in the genitourinary system
conditions, and a non-significant reduction in hospitalizations due to sickness in the circulatory system(Additional file 1: Supplementary Fig. 2 and Table 2). A
reduction in the hospitalization rate of nursing homeresidents before death was also observed (Fig. 1), suggesting an increase in end-of-life care conducted within
nursing homes during the pandemic.
In the following section, we present the results from theevent study. In contrast with the simple DD comparisonin Table 2, which outlines average estimates before andafter the onset of the pandemic, the event study estimatesoffer a more comprehensive insight into how the pandemic influenced the health of nursing home residents
over time by displaying monthly variation in rates of (i)mortality, (ii) hospitalization, and (iii) hospitalization
before death throughout the study period.
Regression results

The results from the event regression specified in Eq. 1
are presented in Fig. 2. This figure illustrates that themortality rates in both cohorts were comparable during
the 6 months in the pre-treatment period (September
2017 to February 2018 (k =  − 5 to 0) in the pre-pandemic
cohort and September 2019 to February 2020 (k =  − 5to 0) in the pandemic cohort). The lack of statisticallysignificant differences in mortality in this period supports the validity of the parallel trend assumption. Afterthe onset of the pandemic, the line continued to fluctuate around the zero line until it became positive, albeitØien et al. BMC Medicine(2024) 22:318

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Mortality

Hospitalization

Hospitalization before death

2

1

10

0

0

−1

−10

Percentage points

1

0

−1

−2
−5

0

5

10

15

−5

0

5

10

15

−5

0

5

10

15

Relative month
Fig. 2 The impact of the pandemic on monthly mortality and healthcare utilization among nursing home residents. Estimated monthly difference(95% confidence interval (CI)) in mortality, hospitalizations, and hospitalizations before death for nursing home residents in the pandemic cohortand pre-pandemic cohort. The x-axis refers to the relative month (k). The pre-pandemic cohort was measured 24 months earlier. The dottedvertical line (k = 0) refers to February 2018 for the pre-pandemic cohort, and February 2020, which is the month before the onset of the pandemic,
for the pandemic cohort

not statistically significant, in August 2020 (k = 6) when
the second wave of the pandemic emerged. However, inthe seasonal difference-in-differences estimates where
all months during fall 2020 are collectively considered,a statistically significant increase in the average monthly
mortality rate for the pandemic cohort of 0.31 percentage
points is observed, representing a 9% increase from thepre-pandemic level (Table 3). The prior increase in themortality rate leveled off during the winter 2020/2021.
By spring 2021, the monthly mortality rates were 0.24
percentage points lower in the pandemic cohort, corresponding to a relative reduction in mortality of 7% in the
pandemic cohort compared to the pre-pandemic cohort(Table 3).
Overall, the event study and seasonal difference-indifferences estimates of mortality fluctuate around zero.
This phenomenon may be attributed to selective survival,
wherein spikes in mortality during one period resultin a relatively healthier population and fewer deaths insubsequent periods. To evaluate whether the later reductions in mortality offset the earlier increases, we present
Kaplan–Meier survival curves in Additional File 1: Supplementary Fig. 3. Notably, the survival curves for the
pandemic and pre-pandemic cohorts were remarkably
similar, with a p-value from a log-rank test of 0.9.
The inpatient hospitalization rate declined by approximately 1 percentage point from February to March andApril 2020 (k = 1, 2) (Fig. 2), before beginning to riseagain in May 2020 (k = 3). On average, the reduction in
the monthly inpatient hospitalization rate for the pandemic cohort in spring 2020 was 0.77 percentage points,
representing a 24% decrease compared to the prepandemic level (Table 3). The reduction was primarily
attributed to decreases in hospitalizations related to respiratory, genitourinary, and circulatory conditions, which
decreased by roughly 49%, 35%, and 30%, respectively,
compared to the pre-pandemic level. While there was aresurgence in hospitalization rates during the summer

3.22

18.40

Hospitalization(all-cause)

Hospitalization
before Mortality− 3.60*** (1.295)− 0.77*** (0.111)

0.11 (0.116)− 0.12*** (0.035)− 0.02 (0.051)

Sickness in geni- 0.34
tourinary system (N)Injury, poisoning, 0.67
and certain otherconsequencesof external causes(S)− 0.27*** (0.046)− 0.04 (0.033)

0.55

Sickness in respiratory system (J)− 0.11*** (0.038)

Sickness in diges- 0.29
tive system (K)0.38

Sickness in circulatory system (I)Hospitalizations (cause specific)

3.49

Mortality (allcause)

Health outcome

Summer 2020

Fall 2020

Winter 2020/2021

Spring 2021

Summer 2021

−3

− 35

− 12

− 49

− 30

− 20

− 24

3

0.04 (0.054)− 0.03 (0.039)− 0.01 (0.038)− 0.09** (0.047)

0.02 (0.047)− 0.18(1.420)− 0.11 (0.119)

0.02 (0.114)

6

−9

−5

− 17

5

−1

−3

0

0.05 (0.057)− 0.06 (0.040)

0.01 (0.038)− 0.07 (0.048)− 0.03 (0.043)

0.41 (1.418)− 0.07 (0.123)

0.31**(0.120)

8

− 17

3

− 13

−7

2

−2

9

0.06 (0.059)− 0.03 (0.040)− 0.06 (0.037)− 0.18 (0.052)− 0.00 (0.043)− 1.56 (1.341)− 0.22* (0.128)

0.13 (0.135)

9

−8

− 19

− 33

0

−8

−7

4

0.11 (0.058)− 0.06 (0.043)

0.02 (0.041)− 0.16 (0.054)− 0.03 (0.045)− 0.12 (1.481)− 0.16 (0.132)− 0.24* (0.134)

17

− 18

6

− 29

−8

−1

−5

−7

0.02 (0.061)− 0.03 (0.046)− 0.02 (0.044)− 0.09 (0.059)− 0.03 (0.046)

1.49 (1.599)− 0.07 (0.140)

0.03 (0.136)

3

− 10

−6

− 17

−9

8

−2

1

DD estimate (SE) Rel. % DD estimate (SE) Rel. % DD estimate (SE) Rel. % DD estimate (SE) Rel. % DD estimate (SE) Rel. % DD estimate (SE) Rel. %Pre % Spring 2020

Table 3 The impact of the pandemic on mortality and healthcare utilization among nursing home residents, by seasonØien et al. BMC Medicine(2024) 22:318
Page 9 of 14Øien et al. BMC Medicine(2024) 22:318

of 2020, the rates leveled off post-summer, maintaining
a 2–7% reduction from the figures observed prior to thepandemic, as documented in Table 3.
The hospitalization rates before death exhibited a similar pattern to general hospitalization rates (Fig. 2). During the spring of 2020, there was a significant relative
decrease in monthly hospitalization rates before death by
20% (Table 3). However, there were no significant differences in hospitalization rates before death between the
pandemic cohort and the pre-pandemic cohort in thesubsequent seasons.
Subgroup analysis by community transmission levels

The results from the event study model, estimated separately for nursing home residents in areas with low,
medium, and high community transmission, are presented in Fig. 3 and Additional File 1: Supplementary
Table 1 (for crude rates, see Additional File 1: Supplementary Fig. 4). The monthly mortality rate of nursing
home residents in areas with relatively high community
transmission rates spiked around the peak of the first andsecond waves of the pandemic (April/May 2020 (k = 2,3)
and December 2020/January 2021 (k = 10, 11), respectively) (Fig. 3). However, as for the rest of the results on
mortality for different levels of community transmission,
the pandemic cohort and the pre-pandemic cohort were
not statistically significantly different (Table A1). The
probability of hospitalization significantly decreased for
all nursing home residents during the first two monthsof the pandemic, regardless of the level of community
transmission (Fig. 3). Interestingly, the estimated difference in hospitalization rates between the pandemic andpre-pandemic cohort in communities with high andmiddle transmission rates returned to the pre-pandemic
level by summer 2020, while hospitalization rates forthe pandemic cohort in communities with relatively lowtransmission rates stabilized at a significantly lower level(Fig. 3, Additional File 1: Supplementary Table 1). In areas
with high levels of community transmission, nursing
home residents were less likely to be hospitalized before
death, particularly during spring and fall 2020 when the
reduction was statistically significant (Additional File 1:
Supplementary Table 1). The results for areas with low
and medium levels of community transmission are lessclear (Fig. 3, Additional File 1: Supplementary Table 1).
The first column shows the average monthly percent
of pandemic residents that died/had at least one hospital admission/had at least one hospital admission
prior to death in the 6 months before the pandemic(i.e., before March 2020), calculated separately for eachoutcome. Difference-in-difference estimates (DD estimates) quantify the change in health outcomes eachseason (March–May (spring); June–August (summer);

Page 10 of 14

September–November (fall); and December–February(winter)), measured as change in percentage points (DDestimates*100). Standard errors (SEs) are clustered on
individuals. In addition to the presentation of results inabsolute terms, relative differences in percent (Rel. %) arealso presented, calculated by dividing the absolute estimate for each of the post-periods by the monthly average
health outcome for the pandemic patients in the periodprior to the pandemic (September 2019–February 2020,
Pre %). Stars are used to denote the conventional levels of
statistical significance, where * corresponds to a p-valueof 0.1 or less, ** to a p-value of 0.05 or less, and *** to a
p-value of 0.01 or less.

Discussion
In this study, we have assessed the impact of the
COVID-19 pandemic on mortality, hospitalization, andpre-death hospitalization rates among nursing homeresidents in Norway during the first 18 months of thepandemic. Notably, our findings reveal no significant
overall increases in mortality rates, even in regions withhigh community transmission. However, certain periods showed noteworthy trends. For instance, a decline
in mortality rates was observed in February 2021, whenmost of the nursing home population had received two
doses of the COVID-19 vaccine. However, this decline
may also be partly attributed to selective survival, as
it occurred after a peak in mortality rates. Following aperiod of stabilization after April 2021, mortality rates
rose again in August 2021, coinciding with the easing ofcommunity restrictions and the emergence of the Delta
variant.
The absence of a clear overall increase in mortality during our study period may be interpreted as an indicationthat Norway’s proactive response, with early implementation of both national and nursing home-specific measures, effectively protected nursing home residents. The
early enforcement of national restrictions was likely crucial in keeping infection rates lower than in most other
European countries throughout the study period [37].
In addition, a comparison of infection rates in the general population with those in nursing home residents in2020 shows that the infection rates among nursing homeresidents were lower (686 per 100,000 among nursing
home residents versus 956 per 100,000 among all Norwegian inhabitants [28, 38]). This suggests that the nursing
home-specific measures also effectively prevented thevirus from entering these facilities.
Another important finding from our study is the substantial decrease in hospitalization rates among nursing
home residents of 24% during the spring of 2020. Thisdecline was primarily attributed to decreases in hospitalizations for respiratory diseases. Following this period,Øien et al. BMC Medicine(2024) 22:318

Page 11 of 14

Low

Low

Low

Mortality

Hospitalization

Hospitalization before death

2
1

10

1
0
0

0

−1
−1

−10

−2
−2

Percentage points

−5

0

5

10

15

−5

0

5

10

15

−5

0

5

10

15

MiddleMiddleMiddleMortality

Hospitalization

Hospitalization before death

2
1
0
−1

1

10

0

0

−1

−10

−2
−5

0

5

10

15

−5

0

5

10

15

−5

0

5

10

15

High

High

High

Mortality

Hospitalization

Hospitalization before death

2
1
0

1

10

0

0
−10

−1

−1

−20

−2

−2
−5

0

5

10

15

−5

0

5

10

15

−5

0

5

10

15

Relative month
Fig. 3 The impact of the pandemic on monthly mortality and healthcare utilization among nursing home residents in areas with low, middle,
and high community transmissions. Estimated monthly difference (95% CI) in mortality, hospitalizations, and hospitalizations before deathfor nursing home residents in the pandemic cohort and pre-pandemic cohort by level of community transmission. The x-axis refers to relative
month. The pre-pandemic cohort was measured 24 months earlier. The dotted vertical line (k = 0) refers to February 2018 for the pre-pandemic
cohort, and February 2020, which is the month before the onset of the pandemic, for the pandemic cohort

hospitalization rates remained stable, with no significant
fluctuations observed after spring 2020.
During the spring of 2020, we also observed a notable 20% decrease in hospitalizations prior to deathamong nursing home residents. This trend was consistent across all areas but was most pronounced inregions with the highest community transmission rates.

This suggests that nursing homes in regions with highcommunity transmission rates were particularly conscientious in adhering to guidelines promoting on-site
treatment for residents, aiming to mitigate hospitalcongestion.
While the high proportion of COVID-19 deaths innursing homes has raised concerns, our study resultsØien et al. BMC Medicine(2024) 22:318

suggest that this metric alone might not fully reflect theeffectiveness of care homes in managing the pandemic.
For instance, in Norway, nursing home fatalities constituted 60% of all COVID-19 deaths, one of the highest
rates among countries within the Organization for Economic Co-operation and Development (OECD) [16]. Yet,
our analysis indicates no significant increase in overall
deaths among nursing home residents during the pandemic compared to the pre-pandemic period.
The study shows that Norway managed to reduce hospitalization rates of nursing home residents in the lastmonths of life without experiencing increased mortality.
A question is whether aging societies and communities
can learn from the pandemic about how to effectively
prevent the spread of contagious diseases to reduce hospitalization rates of nursing home residents and theirassociated costs. Within the evolving landscape of anaging population and the consequent challenges posedby healthcare capacity constraints, the implications oftransitioning end-of-life care from hospitals to nursing
homes emerge as an important topic for future research.
That said, it is important to note that strict infection control measures are likely to have adverse effects on thequality of life of nursing home residents and their families. Restrictions on visitors and communal activities
most likely led to social isolation and loneliness, which
are associated with a wide range of adverse health effects,
such as depression, anxiety, cognitive decline, and evenpremature death [3, 7]. Therefore, policymakers should
consider unintended consequences and quality of life ofnursing home residents and their families when implementing infection control measures in nursing homes.
Some strengths and weaknesses of this study are worth
mentioning. First, a major strength of this study is theaccess to comprehensive administrative data covering the entire nursing home population in Norway andtheir health outcomes over time. This provided us witha unique opportunity to analyze and comprehend thepotential impacts of the COVID-19 pandemic at the
population level. Additionally, by comparing the healthoutcomes of nursing home residents exposed to the pandemic with nursing home residents two years earlier, we
isolated the impact of the pandemic from secular trends
in health outcomes by both age, duration of study, andperiod in a novel way. There are also some limitations.
First, Norway has a relatively small population, resulting
in fewer nursing home residents. This affects the precisionof our estimates, necessitating caution when interpreting
the results. Additionally, as a high-income country withextensive healthcare resources, the external validity of ourfindings to lower-income regions may be limited. Second,
we were not able to disentangle the impacts of different
community-wide and nursing home-specific preventative

Page 12 of 14

control measures. This is an inherent challenge in observational studies of pandemic response because many ofthese measures were implemented at the same time, making it difficult to identify their individual effects. Future
pandemic responses should, where feasible, stagger the
implementation of measures making it easier for future
research to evaluate the impact of different measures.
Third, we did not have access to comorbidity informationin the nursing home registry. We therefore relied on priorhospitalization and mortality rates as proxies, which limits the analysis regarding health disparities between the
pre-pandemic and the pandemic cohort. Fourth, our analysis does not account for variations in virus spread withinmunicipalities due to the unavailability of specific location
data for individual nursing homes. A fifth limitation of ourstudy was the inability to follow cohorts through the Delta
and Omicron waves due to data constraints. Throughout
2020 and 2021, the incidence rate of SARS-CoV-2 among
nursing home residents in the most affected communities fluctuated between 8.75 and 14.30 cases per 100 people. However, during the Omicron surge from January to
March 2022, Norway’s national incidence rate soared to71.53 cases per 100 individuals [39]. This spike was largely
driven by Omicron’s higher transmissibility and the easingof infection control measures in February 2022. However,since Omicron was associated with a lower risk of severe
outcomes and the population had a high level of vaccination, mortality rates did not see a parallel rise. Nonetheless, a report by a report from the Norwegian Institute of
Public Health indicates an increase in excess mortality in
2022, suggesting that this may be due in part to a backlog
of deaths that were postponed by the stringent controls
earlier in the pandemic [40].

Conclusions
Our study contributes to the current understanding of
how the COVID-19 pandemic has impacted the nursing home population. Contrary to expectations, ourfindings reveal no clear evidence of excess mortality
in Norway during the pandemic, even in regions withrelatively high rates of community transmission. These
results suggest that the early implementation of nationwide and nursing home-specific infection control measures in Norway during the pandemic protected nursing
home residents. Moreover, our research highlights thepotential for infection prevention and control measures
to reduce hospitalization rates among nursing homepatients, without a corresponding rise in mortality. This
insight is particularly valuable for policymakers looking to reduce expensive hospital stays. While there havebeen concerns regarding the disproportionate numberof COVID-19 deaths in nursing homes, our study suggests that this metric may not accurately reflect theØien et al. BMC Medicine(2024) 22:318

performance of nursing homes in preventing the spreadof the virus. The disproportionate share of total pandemic deaths attributed to nursing home residents canlargely be explained by nursing home residents being
susceptible to infections.
Abbreviations
CI	Confidence interval
COVID-19	Coronavirus disease 2019DD	Difference-in-differences
ICD-10	International Classification of Diseases, version 10.
OECD	The Organization for Economic Co-operation and Development
SD	Standard deviation
UK	United Kingdom
US	United States

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03523-8.
Additional file 1: Supplementary Fig. 1. “Classification of SARS-CoV-2 Community Transmission in Norway. This map illustrates the levels of community transmission across municipalities, categorized as low, medium, andhigh.” Supplementary Fig. 2. “Crude monthly percentages of cause-specifichospitalizations among nursing home residents.” Supplementary Fig. 3.
“Kaplan–Meier Survival Curves for Pre-Pandemic and Pandemic Cohorts”
Supplementary Fig. 4. “Crude monthly percentages of mortality and healthcare utilization among nursing home residents in areas with low, middle
and high community transmissions.” Supplementary Table 1. “The impactof the pandemic on mortality and health care utilization among nursing
home residents in areas with low, medium, and high community transmission, by season.”
Acknowledgements
We would like to thank Hege Marie Gjefsen, Kjetil Elias Telle, Håkon Bøås, Trine
Hessevik Paulse, Petter Langlete, and Hanne-Merete Eriksen-Volle for helpful
discussions during this project. A special thanks to Angela Susan Labberton
for reviewing our manuscript for readability and grammar. The interpretation
and reporting of the data are the sole responsibility of the authors.
Authors’ contributions
HØ and MG designed the study. All authors had access to data in the study
and took joint responsibility for the integrity of the data and the accuracy ofthe data analysis. JG and KDS performed the statistical analyses. HØ, MG, andTAR drafted the manuscript. All authors contributed with conceptual design,
analyses, and interpretation of the results. All authors critically revised themanuscript for important intellectual content. All authors read and approved
the final manuscript.
Funding
Open access funding provided by Norwegian Institute of Public Health (FHI)
The current study was internally funded by the Norwegian Institute of Public
Health and by the Research Council of Norway (grant 256570).
Availability of data and materials
The findings of this study are based on data from the Norwegian Emergency
Preparedness Register, which aggregates individual-level data across various Norwegian administrative databases, including those managed by the
Norwegian Directorate of Public Health, Statistics Norway, the NorwegianInstitute of Public Health, and the Norwegian Tax Administration. Due torestrictions, these data, accessed under a specific license for this study, are notopenly available. Researchers wishing to use this data must submit a formal
application, which is accessible at https://​helse​data.​no. This application should
outline a comprehensive research proposal, detailing the project’s objectives
and methodologies. Approval from the Regional Committees for Medicaland Health Research Ethics is required, application for approval is available

Page 13 of 14

at rekportalen.no. The authors are willing to facilitate access to the data forinterested researchers upon a justified request.

Declarations
Ethics approval and consent to participate
The establishment of an emergency preparedness register forms part of thelegally mandated responsibilities of The Norwegian Institute of Public Health(NIPH) during epidemics [31]. The legal basis is the Norwegian Health Preparedness Act §2–4. Institutional board review was conducted by The Ethics
Committee of South-East Norway, and the committee confirmed (June 4th,
2020, #153204) that external ethical board review was not required. All methods were carried out in accordance with relevant guidelines and regulations.
Consent for publication
We did not obtain verbal or written consent from the study population
because the current study was based on routinely collected and anonymized
register data. The law regulating official statistics in Norway implies thatanonymized register data can be used for research. The law of statistics
combined with the Ethics Committee of South-East Norway confirmed that
informed consent was not necessary.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Cluster of Health Services Research, Norwegian Institute of Public Health,Oslo, Norway. 2 Breast Cancer Screening, Norwegian Institute of Public Health,Oslo, Norway. 3 Department of Health Management and Health Economics,
University of Oslo, Oslo, Norway.
Received: 21 December 2023 Accepted: 9 July 2024

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